Submitted:
14 October 2026
Posted:
15 October 2026
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Abstract
AI-assisted phytochemical nomination requires independent checks of chemical identity and supporting evidence. This computational case study examines AIIP-BAI-6C-DMAE-002, the proposed 2-(N,N-dimethylamino)ethyl ester of baicalin's glucuronide C-6″ carboxyl group, originating in an author-held AI immunopharmacology book manuscript. The nominated connectivity was reconciled with inconsistent formula and mass entries in that source. RDKit 2025.09.6 generated identifiers, physicochemical descriptors and comparisons with eight related structures. The encoded neutral candidate has formula C25H27NO11, molecular weight 517.49 g/mol, monoisotopic mass 517.1584 Da and InChIKey BIUNXNZQUMPJLF-LYVDORBWSA-N. Relative to baicalin, calculated polar surface area decreases from 187.12 to 179.36 Ų, while neutral-form Crippen cLogP changes from 0.1422 to 0.1624. These results do not establish improved absorption. Public literature and patent searching found established esterification and amino-substitution precedents but no individualised exact disclosure in the retrieved evidence; candidate PubChem InChIKey queries returned no matching record. Novelty remains provisional pending comprehensive structure and Markush analysis. The historical AI-generation trace is incomplete, and no synthesis, binding or efficacy is reported. The study provides a reproducible chemical assessment and a validation framework for a phytochemical-derived immunopharmacology hypothesis.
Keywords:
artificial intelligence
; baicalin
; chemical identity
; aminoethyl ester
; cheminformatics
; structural novelty
; immunopharmacology
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